ORCID Profile
0000-0002-0248-8738
Current Organisations
National Center For Child Health and Development
,
Universidade Federal do Espírito Santo
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Publisher: Magnolia Press
Date: 23-11-2016
DOI: 10.11646/ZOOTAXA.4196.3.9
Abstract: The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007 Donegan 2008, 2009 Nemésio 2009a–b Dubois 2009 Gentile & Snell 2009 Minelli 2009 Cianferoni & Bartolozzi 2016 Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016.
Publisher: FapUNIFESP (SciELO)
Date: 03-2021
Abstract: RESUMO Definir qualidade no ensino superior não é algo trivial, já que o próprio conceito de qualidade pode ter diferentes significados em diferentes contextos. A despeito das falhas conceituais e metodológicas que envolvem os rankings universitários, eles são uma realidade que veio para ficar. Eles necessitam de dados sobre as mais ersas dimensões da vida universitária, mas a falta de métricas específicas e a própria mensuração de impactos, são enormes desafios. A proposta deste trabalho é discutir as bases conceituais para um sistema de gestão de dados unificado entre as Instituições Federais de Ensino Superior (IFES). Sugere-se a criação de uma unidade de inteligência capaz de assegurar e aprofundar o autoconhecimento institucional por meio do monitoramento de indicadores e ulgação de dados para subsidiar o diálogo permanente e necessário intra e, principalmente, extra muros. Para tanto, faz-se uma discussão sobre dois aspectos fundamentais: a importância da governança e os aspectos operacionais intrínsecos de uma unidade de gestão de dados em cada IFES. Em relação à governança, discute-se a missão e o papel dos atores envolvidos e quanto à operacionalização, discute-se a definição de conceitos, os instrumentos de coleta e análise de dados, a criação de indicadores críticos e a difusão da informação. As IFES já coletam, armazenam e fornecem dados institucionais sistematizados a ersos órgãos de controle e fomento. No entanto, a pulverização de fontes e formatos, a incompletude e a descontinuidade temporal e a falta de estruturação dos dados nos diferentes sistemas limitam sua interoperabilidade. A constituição de uma unidade que promova a curadoria e processamento de dados estratégicos com base na autonomia e acesso ágil e simples de informações é, portanto, fundamental.
Publisher: Springer Science and Business Media LLC
Date: 27-05-2021
DOI: 10.1007/S40262-021-01033-X
Abstract: Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in in idual neonates. Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. In idual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and erse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods. The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods. A combined population pharmacokinetic and machine learning approach provided improved predictions of in idual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, in idual clearance can be predicted a priori using our model code combined with demographic data.
Publisher: Oxford University Press (OUP)
Date: 02-05-2019
DOI: 10.1093/JAC/DKZ158
Abstract: In the absence of consensus, the present meta-analysis was performed to determine an optimal dosing regimen of vancomycin for neonates. A ‘meta-model’ with 4894 concentrations from 1631 neonates was built using NONMEM, and Monte Carlo simulations were performed to design an optimal intermittent infusion, aiming to reach a target AUC0–24 of 400 mg·h/L at steady-state in at least 80% of neonates. A two-compartment model best fitted the data. Current weight, postmenstrual age (PMA) and serum creatinine were the significant covariates for CL. After model validation, simulations showed that a loading dose (25 mg/kg) and a maintenance dose (15 mg/kg q12h if weeks PMA and 15 mg/kg q8h if ≥35 weeks PMA) achieved the AUC0–24 target earlier than a standard ‘Blue Book’ dosage regimen in % of the treated patients. The results of a population meta-analysis of vancomycin data have been used to develop a new dosing regimen for neonatal use and to assist in the design of the model-based, multinational European trial, NeoVanc.
Publisher: Hindawi Limited
Date: 27-04-2021
DOI: 10.1111/JZS.12477
Location: United States of America
Location: No location found
No related grants have been discovered for Yuri Leite.